Identifying microbial drivers in biological phenotypes with a Bayesian Network Regression model

1. In Bayesian Network Regression models, networks are considered the predictors of continuous responses. These models have been successfully used in brain research to identify regions in the brain that are associated with specific human traits, yet their potential to elucidate microbial drivers in biological phenotypes for microbiome research remains unknown. In particular, microbial networks are challenging due to their high-dimension and high sparsity compared to brain networks. Furthermore, unlike in brain connectome research, in microbiome research, it is usually expected that the presence of microbes have an effect on the response (main effects), not just the interactions. 2. Here, we develop the first thorough investigation of whether Bayesian Network Regression models are suitable for microbial datasets on a variety of synthetic and real data under diverse biological scenarios. We test whether the Bayesian Network Regression model that accounts only for interaction effects (edges in the network) is able to identify key drivers (microbes) in phenotypic variability. 3. We show that this model is indeed able to identify influential nodes and edges in the microbial networks that drive changes in the phenotype for most biological settings, but we also identify scenarios where this method performs poorly which allows us to provide practical advice for domain scientists aiming to apply these tools to their datasets. 4. BNR models provide a framework for microbiome researchers to identify connections between microbes and measured phenotypes. We allow the use of this statistical model by providing an easy-to-use implementation which is publicly available Julia package at https://github.com/solislemuslab/BayesianNetworkRegression.jl.Comment: 62 pages, 49 figure

Bayesian Conditional Auto-Regressive LASSO Models to Learn Sparse Biological Networks with Predictors

Microbiome data analyses require statistical models that can simultaneously decode microbes' reaction to the environment and interactions among microbes. While a multiresponse linear regression model seems like a straight-forward solution, we argue that treating it as a graphical model is flawed given that the regression coefficient matrix does not encode the conditional dependence structure between response and predictor nodes because it does not represent the adjacency matrix. This observation is especially important in biological settings when we have prior knowledge on the edges from specific experimental interventions that can only be properly encoded under a conditional dependence model. Here, we propose a chain graph model with two sets of nodes (predictors and responses) whose solution yields a graph with edges that indeed represent conditional dependence and thus, agrees with the experimenter's intuition on average behavior of nodes under treatment. The solution to our model is sparse via Bayesian LASSO and is also guaranteed to be the sparse solution to a Conditional Auto-Regressive (CAR) model. In addition, we propose an adaptive extension so that different shrinkage can be applied to different edges to incorporate edge-specific prior knowledge. Our model is computationally inexpensive through an efficient Gibbs sampling algorithm and can account for binary, counting and compositional responses via appropriate hierarchical structure. Finally, we apply our model to a human gut and a soil microbial composition datasets

Análisis jurídico de la publicidad institucional: estudio de los bienes propuestos en la campaña Marca Perú

Creatives must ensure a high degree of persuasion. However, this persuasive purpose must be consistent with the pursuit of true communication that tends to seek common training community well. Thus to speak of a quality advertising campaign is needed not only to have technical or pragmatic elements effective, but are cared for evaluative elements. In the present investigation was considered the analysis of television advertising pieces of the launch campaign Marca Peru from the perspective of Communication Law, which allows to study each constituent elements of the message, in order to understand how good publicity may contribute to the formation of community.Los mensajes publicitarios deben procurar un alto grado de persuasión. Sin embargo, esta finalidad persuasiva debe ser acorde a la búsqueda de una comunicación verdadera que tienda al bien común para procurar la formación de comunidad. Así, para hablar de una campaña publicitaria de calidad se necesita no solo que hayan elementos técnicos y pragmáticos efectivos, sino también que se atienda a elementos valorativos. En la presente investigación se ha considerado el análisis de las piezas publicitarias televisivas de la campaña de Marca Perú desde el enfoque del Derecho de la Comunicación, que permite estudiar cada uno de elementos constitutivos del mensaje, con el fin de comprender cómo una buena publicidad puede contribuir a la formación de comunidad

MiNAA: Microbiome Network Alignment Algorithm

Our Microbiome Network Alignment Algorithm (MiNAA) aligns two microbial networks using a combination of the GRAph ALigner (GRAAL) algorithm and the Hungarian algorithm. Network alignment algorithms find pairs of nodes (one node from the first network and the other node from the second network) that have the highest similarity. Traditionally, similarity has been defined as topological similarity such that the neighborhoods around the two nodes are similar. Recent implementations of network alignment methods such as NETAL and L-GRAAL also include measures of biological similarity, yet these methods are restricted to one specific type of biological similarity (e.g. sequence similarity in L-GRAAL). Our work extends existing network alignment implementations by allowing any type of biological similarity to be input by the user. This flexibility allows the user to choose whatever measure of biological similarity is suitable for the study at hand. In addition, unlike most existing network alignment methods that are tailored for protein or gene interaction networks, our work is the first one suited for microbiome networks

Finanzas : Apalancamiento operativo y financiero Banco de América Central (BAC),S.A.periodo 2014-2015

El presente trabajo tiene como objetivo demostrar mediante el desarrollo de un caso práctico el funcionamiento del Apalancamiento Financiero, Operativo y Total en una Institución Financiera, considerando que esta temática contribuye a expandir el conocimiento financiero - contable en el campo bancario. Mediante la exhaustiva obtención y lectura de información en Internet y bibliografía recomendada sobre la temática así como el gentil apoyo del Vice – Gerente de Operaciones de Banco de América Central (BAC), S.A fue posible establecer una amplia base de información sobre la cual deriva la preparación del presente trabajo. El Apalancamiento Financiero y Operativo es una herramienta de gestión empresarial de relevante importancia al momento de definir la estructura de Capital que adoptar a una Empresa en función de su estrategia de crecimiento y apetito de riesgo que esté dispuesta a asumir. La adopción de costos fijos por variables o el nivel de gasto financiero permisible por la obtención de financiamiento es posible medirlo haciendo uso del Apalancamiento

Unsupervised Learning of Phylogenetic Trees via Split-Weight Embedding

Unsupervised learning has become a staple in classical machine learning, successfully identifying clustering patterns in data across a broad range of domain applications. Surprisingly, despite its accuracy and elegant simplicity, unsupervised learning has not been sufficiently exploited in the realm of phylogenetic tree inference. The main reason for the delay in adoption of unsupervised learning in phylogenetics is the lack of a meaningful, yet simple, way of embedding phylogenetic trees into a vector space. Here, we propose the simple yet powerful split-weight embedding which allows us to fit standard clustering algorithms to the space of phylogenetic trees. We show that our split-weight embedded clustering is able to recover meaningful evolutionary relationships in simulated and real (Adansonia baobabs) data

Episodic and semantic autobiographical memory in temporal lobe epilepsy

Autobiographical memory (AM) is understood as the retrieval of personal experiences that occurred in specific time and space. To date, there is no consensus on the role of medial temporal lobe structures in AM. Therefore, we investigated AM in medial temporal lobe epilepsy (TLE) patients. Twenty TLE patients candidates for surgical treatment, 10 right (RTLE) and 10 left (LTLE), and 20 healthy controls were examined with a version of the Autobiographical Interview adapted to Spanish language. Episodic and semantic AM were analyzed during five life periods through two conditions: recall and specific probe. AM scores were compared with clinical and cognitive data. TLE patients showed lower performance in episodic AM than healthy controls, being significantly worst in RTLE group and after specific probe. In relation to semantic AM, LTLE retrieved higher amount of total semantic details compared to controls during recall, but not after specific probe. No significant differences were found between RTLE and LTLE, but a trend towards poorer performance in RTLE group was found. TLE patients obtained lower scores for adolescence period memories after specific probe. Our findings support the idea that the right hippocampus would play a more important role in episodic retrieval than the left, regardless of a temporal gradient.Fil: Múnera Martínez, Claudia Patricia. Universidad de Buenos Aires. Facultad de Medicina; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos "Ramos Mejía"; ArgentinaFil: Lomlomdjian, Ana Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Medicina; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos "Ramos Mejía"; ArgentinaFil: Gori, María Belén. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos "Ramos Mejía"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad de Buenos Aires. Facultad de Medicina; ArgentinaFil: Terpiluk, Veronica. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos "Ramos Mejía"; Argentina. Universidad de Buenos Aires. Facultad de Medicina; ArgentinaFil: Medel, Nancy Ruth. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos "Ramos Mejía"; Argentina. Universidad de Buenos Aires. Facultad de Medicina; ArgentinaFil: Solis, Patricia. Universidad Nacional Arturo Jauretche. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos "Ramos Mejía"; Argentina. Universidad de Buenos Aires. Facultad de Medicina; ArgentinaFil: Kochen, Sara Silvia. Universidad de Buenos Aires. Facultad de Medicina; Argentina. Universidad Nacional Arturo Jauretche. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Provincia de Buenos Aires. Ministerio de Salud. Hospital Alta Complejidad en Red El Cruce Dr. Néstor Carlos Kirchner Samic. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Unidad Ejecutora de Estudios en Neurociencias y Sistemas Complejos; Argentina. Gobierno de la Ciudad de Buenos Aires. Hospital General de Agudos "Ramos Mejía"; Argentin